This invention relates to any particular complex product that is susceptible to many types of desired selectable configurations. A sales method and sales system are developed to establish a guarantee of functionality for each specific configuration of the complex product.
The co-pending application U.S. Ser. No. 9/249,171 xe2x80x9cEstimator Program for Estimating the Availability of an Application Program That Runs in a Cluster of at Least Two Computersxe2x80x9d, referenced above involves an estimator program to perform method steps for estimating the availability of an application program that runs on any server in a cluster of at least two servers. By xe2x80x9cavailability of an application programxe2x80x9d is meant the probability that at any particular time instance, at least one of the servers in a cluster (server farm) will actually be servicing requests from external workstations able to use the application program.
In one embodiment, the so-called estimator program begins by receiving input parameters which include (i) multiple downtime periods for each computer in the cluster (server farm) that occur at respective frequencies due to various downtime sources, and (ii) an application failover time period for switching the running of the application program from any one computer to another operable computer. From these input parameters, the described estimator program estimates first and second annual stoppage times, then determines the availability of the application program on the cluster of computers which is derived from the sum of the first and second annual stoppage times.
Thus, as discussed, the estimator program of the previously-described application, U.S. Ser. No. 09/249,171, estimated a first annual stoppage time for the application program due solely to the concurrent stoppage of all of the computers, as a function of the ratio of a single computer virtual downtime period over the single computer virtual time between stops. Then subsequently, the estimator program was used to estimate a second annual stoppage time for the application program, due solely to switching the running application program from one computer to another computer as a function of the single virtual stoppage rate and the application failover time period. From this, the estimator program determined the availability of the application program on the cluster of computers by deriving the sum of the first and second annual stoppage times.
The estimator program method was based on the assumption that xe2x80x9capplication availabilityxe2x80x9d was to be determined from four factors that were:
(i) single-server hardware reliability;
(ii) maintenance, support, and service strategies;
(iii) user application and environment;
(iv) failover or system reconnection mechanism and application recovery mechanism.
The prior estimation parameters that were described in the co-pending application, U.S. Ser. No. 08/550,603, did not take into consideration the total number of operating Server Farm clients and the normal single server workload of users involved with each single server. Further, this earlier application did not provide a recommendation or estimate regarding the Server Farm (or cluster) configuration or the number of servers required in the Server Farm (or cluster), which would meet the customers"" performance and redundancy level requirements, nor did it establish an optimum farm configuration. Nevertheless, calculated availability or system reliability could eventually be used as a guarantee criterion for guarantee design and configuration offering. But, in the above-cited application, U.S. Ser. No. 08/550,603, the system configuration had been predefined and limited to a single server or a cluster of two servers.
Here below, is an example of a particular guarantee for the predefined system such as a single server or, a cluster of two servers described in U.S. Ser. No. 09/249,171 that was offered by one of the computer vendors (Unisys Corporation). The vendor company guarantees that a single server or two-server cluster systems will xe2x80x9cexperience no more than one unplanned system failure per yearxe2x80x9d. If this commitment is not met, the company will issue particular credits for each system type: $1,000 credit for a 4-processor single server system, $2,000 credit for an 8-processor single server system, $5,000 credit for a 4-processor clustered server system, $10,000 credit for a 8-processor clustered server system.
The method of the co-pending application U.S. Ser. No. 09/443,926, entitled xe2x80x9cMethod for Estimating the Availability of an Operating Server Farmxe2x80x9d, extended the area of the original method application for Server Farms designed to serve user communities with a required particular number of customers xe2x80x9cnxe2x80x9d. This method involving the Server Farm size and availability calculations is based on (1) the single server parameters such as (a) the meantime to failure (MTTF), (b) the meantime to repair (MTTR), and (c) the single server application performance benchmarks, and (2) individual customer preferential requirements, involving (a) the total number of Server Farm application users and (b) a desirable redundancy level.
This estimation method (of co-pending U.S. Ser. No. 09/443,926) for xe2x80x9cavailabilityxe2x80x9d uses the following definition of Server Farm availability. This definition is the probability that a Server Farm provides access to applications and data for a particular minimum number of users. As soon as the Server Farm cannot serve this particular minimum number of users, it is considered failed. When some of the users have lost connections but can reconnect to other servers and continue to work, and the majority of users do not experience any interruptions in their work, the farm is not considered failed, if it can still serve this particular minimum number of users.
A widely used approach to improve a system""s availability beyond the availability of a single system is by using Server Farms with redundant servers. In this case, if one of the farm""s servers fails, the xe2x80x9cunluckyxe2x80x9d users connected to this server will lose their connections, but they will have an opportunity to reconnect to other servers in the farm and get access to their applications and data. If all of the xe2x80x9cunluckyxe2x80x9d users get access to their applications and data, the farm is considered xe2x80x9cavailable.xe2x80x9d If at least one of the xe2x80x9cunluckyxe2x80x9d users fails to get access to his/her applications and data, it means that the Server Farm""s redundancy was exhausted and the Server Farm is considered failed.
The parameters for MTTF and MTTR can be estimated, as indicated in the cited prior U.S. Ser. No. 09/249,171, as a single computer virtual time between failures and a single computer virtual downtime period, respectively, for a particular application and user environment.
Therefore, the availability estimation method of the prior application U.S. Ser. No. 09/443,926 allows one to estimate such parameters of the Server Farm as a server farm configuration (the total number of servers in the farm and the number of redundant servers in the farm), Server Farm availability, and Server Farm downtime, based on a set of input data. At the same time, however, this method does not provide any recommendations about xe2x80x9coptimumxe2x80x9d combinations of the Server Farm parameters that can be chosen at the Server Farm planning or design stage.
The method of the co-pending application U.S. Ser. No. 09/705,441, entitled xe2x80x9cMethod for Server Farm Configuration Optimizationxe2x80x9d, added a new configuration feature to the original method of the prior application U.S. Ser. No. 09/443,926 by involving the Server Farm size optimization based on the input data that include single server parameters similar to the prior application U.S. Ser. No. 09/443,926 and at least two new extra parameters: (i) single server cost and (ii) the downtime cost. Additionally, this method included steps of selecting an optimization parameter, selecting an optimization criterion, and using an optimization technique procedure to find the optimum value of the optimization parameter.
The method of another co-pending application U.S. Ser. No. 09/705,441, entitled xe2x80x9cMethod for Server Metafarm Configuration Optimizationxe2x80x9d, describes another example of a complex product configuration. The Metafarm is a group of identical Server Farms that uses a workload balancing mechanism that distributes requests for services or applications to the available servers. As a result of the configuration procedure, a Metafarm is divided into the xe2x80x9coptimum numberxe2x80x9d of the Server Farms. The Metafarm availability is the probability that a Metafarm provides access to applications and data for a particular minimum number of users. The Metafarm availability value can be used as one of the optimization procedure constraints (for example, as a xe2x80x9cgoalxe2x80x9d) and, therefore, it influences configuration procedure results.
All of the cited co-pending applications provide configuration design methods for complex products based on product requirements. At the same time, however, these methods do not provide any recommendations about Server Farm or Metafarm xe2x80x9cguaranteesxe2x80x9d similar to the guarantee described above. Estimated system parameters such as Server Farm (or Metafarm) availability or Server Farm (or Metafarm) downtime can significantly vary for different product configurations. The complexity of the configurations complicates the guarantee definition for each of the possible configurations.
The presently described new method involving the complex products configuration and guarantee generation is based on the input data that includes customer requirements. In one embodiment the data includes single server parameters similar to the prior applications (U.S. Ser. Nos. 09/249,171, 09/443,926, 09/474,706, and U.S. Ser. No. 09/705,441) and at least one complex product guarantee threshold for the guarantee criterion. This new method includes newly added steps of guarantee method design and risk/cost analysis of the complex product for the specific customer that includes (i) selecting complex product requirements, (ii) selecting a guarantee criterion, (iii) inputting data in a configuration xe2x80x9ccalculatorxe2x80x9d, (iv) configuration calculation, (v) calculating the value of the guarantee criterion that depends on the complex product configuration, and (vi) generation of the guarantee recommendation for each product configuration.
While the present invention may be shown in a preferential embodiment for a Server Farm that should serve a particular number of concurrent users, it is not limited thereto, and can be used for any other complex products where (i) the product configuration is designed based on the customer requirements, (ii) the variety of configurations is not predefined, and (iii) complex product characteristics such as product availability or product downtime can be estimated and significantly vary for different product configurations. For example, a complex product could involve an automobile which can be purchased in many different configurations such as (i) engine (4 cylinder, 6 cylinder or 8 cylinder); (ii) two-door or four-door; (iii) manual or automatic transmission; (iv) two or four-wheel drive; (v) automatic four-wheel brakes or standard brakes; (vi) or a variety of other additives and features which make the final product involve a specially-chosen configuration.
Thus, in the example of a selected automobile configuration, the guarantee of proper functionality will depend on the reliability parameters involved in the various components which make-up the complex product.
This invention relates to a sales method and sales system of complex products whose configuration is designed based on customer requirements. Another example of this kind of complex product is a server farm that consists of several servers that should provide a required performance level for a particular number of concurrent users. A value of a designated guarantee criterion for the server farm such as system availability can be evaluated only after the server farm configuration is determined. Therefore, for each combination of the specific customer requirements, a system configuration and corresponding value of the guarantee criterion will be generated. Then, this value is used for the customer xe2x80x9cremedy calculationsxe2x80x9d. For example, it can be compared with predefined thresholds that correspond to particular algorithms of the remedy calculations.
Thus the object of the present invention is to provide a method of guarantee generation by designing the complex product configuration based on the customer requirements. The newly-described method will generate a guarantee recommendation for the selected set of input data and the selected guarantee criterion.
In accordance with the present invention, a novel sales system of complex products whose configuration is designed based on the customer requirements provides method steps for generating guarantee offers. A value of the guarantee criterion for the complex product (such as product availability) can be evaluated only after the complex product configuration is determined. Therefore, for each combination of the customer requirements, a system configuration and a corresponding value of the guarantee criterion will be generated. Then, this value is used for the customer remedy calculations. For example, it can be compared with predefined thresholds that correspond to particular algorithms of designated remedy calculations.
In one particular embodiment, an example of this kind of a complex product is a Server Farm that consists of several servers that should provide a required predetermined performance level. The Server Farm vendor offers a certain minimum number of the server farm failures per year as a guarantee criterion. The guarantee method shown herein provides two different guarantee thresholds: (i) zero server farm failures per year; and (ii) one server farm failure per year. The remedy is calculated as a function of the threshold and the number of servers that make up the server farm.
The present method uses the fact that for some configurable complex products, the variety of possible configurations is not predefined and complex product characteristics such as product performance, availability or product downtime can be estimated for any custom-generated product configuration. These product characteristics significantly vary for different product configurations.